For any library that invests in IGI Global's InfoSci-Books and/or InfoSci-Journals databases, IGI Global will match the library’s investment with a fund of equal value to go toward subsidizing the OA APCs for their faculty patrons when their work is submitted/accepted under OA into an IGI Global journal.

Subscribe to the Latest Research Through IGI Global's InfoSci-OnDemand Plus

InfoSci®-OnDemand Plus, a subscription-based service, provides researchers the ability to access full-text content from over 100,000+ peer-reviewed book chapters and 25,000+ scholarly journal articles that spans across 350+ topics in 11 core subjects. Users can select articles or chapters that meet their interests and gain access to the full content permanently in their personal online InfoSci-OnDemand Plus library.

Purchase the Encyclopedia of Information Science and Technology, Fourth Edition

and Receive Complimentary E-Books of Previous Editions

When ordering directly through IGI Global's Online Bookstore, receive the complimentary e-books for the first, second, and third editions with the purchase of the Encyclopedia of Information Science and Technology, Fourth Edition e-book.

Create a Free IGI Global Library Account to Receive a 25% Discount on All Purchases

Exclusive benefits include one-click shopping, flexible payment options, free COUNTER 5 reports and MARC records, and a 25% discount on single all titles, as well as the award-winning InfoSci®-Databases.

Abstract

Privacy attack on individual records has great concern in privacy preserving data publishing. When an intruder who is interested to know the private information of particular person of his interest, will acquire background knowledge about the person. This background knowledge may be gained though publicly available information such as Voter's id or through social networks. Combining this background information with published data; intruder may get the private information causing a privacy attack of that person. There are many privacy attack models. Most popular attack models are discussed in this chapter. The study of these attack models plays a significant role towards the invention of robust Privacy preserving models.

Introduction

Data Owner: The one who owns the data or the data is about that individual

3.

Data Recipient: The one who can access the published data

Publisher will collect the data from data owners. Data owners will have trust over the publisher and give their data. Publisher will publish it by removing the data which may directly leads to breach of privacy of data owners. The data recipients will receive the data from publisher and use the data for analysis purpose to process the data to come out with analogy or decision making.

Publisher before publishing the data should remove the attributes which directly identifies the individual. For example, consider hospital data related to patient entity. Name and complete address will directly identify the individual. So, such attributes will be removed from the data base and rest of attributes will be published. But, this is not sufficient to provide privacy. The person who is interested to know the private information of other individual, can threat privacy of that person. This can be achieved by having background knowledge (Martin, D.J. 2007) about that person and linking the same with the published data. Consider the following data in table form:

Figure 1.

Privacy preserving data publishing

Intruder, who is interested in private information of other individual, will have background knowledge (RASTOGI V, 2007) about the person like, where he lives. The area zip code will be 146254. He knows that, that person’s age is in 30’s. If this background information is linked to the Table 1, intruder can easily conclude that, that person is having hepatitis disease causing a privacy breach. Many privacy preserving methods (Hua-jin Wang 2007, Qinghai Liu 2014) came into existence to avoid privacy breach. Some popular traditional methods are: Perturbation (DUNCAN G 1998, Xiao-Bai Li 2006) and Inference control (Haibing Lu; 2008, Yingjiu Li 2006, R. Brand 2002).